A. Mouton
On the Relevance of Denoising and Artefact Reduction in 3D Segmentation and Classification within Complex Computed Tomography Imagery
Mouton, A.; Breckon, T.P.
Abstract
We evaluate the impact of denoising and Metal Artefact Reduction (MAR) on 3D object segmentation and classification in low-resolution, cluttered dual-energy Computed Tomography (CT). To this end, we present a novel 3D materials-based segmentation technique based on the Dual-Energy Index (DEI) to automatically generate subvolumes for classification. Subvolume classification is performed using an extension of Extremely Randomised Clustering (ERC) forest codebooks, constructed using dense feature-point sampling and multiscale Density Histogram (DH) descriptors. Within this experimental framework, we evaluate the impact on classification accuracy and computational expense of pre-processing by intensity thresholding, Non-Local Means (NLM) filtering, Linear Interpolation-based MAR (LIMar) and Distance-Driven MAR (DDMar) in the domain of 3D baggage security screening. We demonstrate that basic NLM filtering, although removing fewer artefacts, produces state-of-the-art classification results comparable to the more complex DDMar but at a significant reduction in computational cost - bringing into question the importance (in terms of automated CT analysis) of computationally expensive artefact reduction techniques. Overall, it was found that the use of MAR pre-processing approaches produced only a marginal improvement in classification performance (< 1%) at considerable additional computational cost (> 10×) when compared to NLM pre-processing.
Citation
Mouton, A., & Breckon, T. (2019). On the Relevance of Denoising and Artefact Reduction in 3D Segmentation and Classification within Complex Computed Tomography Imagery. Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, 27(1), 51-72. https://doi.org/10.3233/xst-180411
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 15, 2018 |
Online Publication Date | Apr 1, 2019 |
Publication Date | 2019 |
Deposit Date | Sep 11, 2018 |
Publicly Available Date | Sep 13, 2018 |
Journal | Journal of X-Ray Science and Technology |
Print ISSN | 0895-3996 |
Electronic ISSN | 1095-9114 |
Publisher | IOS Press |
Peer Reviewed | Peer Reviewed |
Volume | 27 |
Issue | 1 |
Pages | 51-72 |
DOI | https://doi.org/10.3233/xst-180411 |
Public URL | https://durham-repository.worktribe.com/output/1320544 |
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Copyright Statement
The final publication is available at IOS Press through https://doi.org/10.3233/XST-180411
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